Private beta
QDivZero opens private beta for capacity-priced AI infrastructure
The private beta for QDivZero is now open: one operating layer for model execution, routing, retrieval, and guardrails without per-token billing.
QDivZero is now open for private beta.
QDivZero launches as Valendra's cloud operating layer for AI enthusiasts and teams that want to deploy models without worrying about per-token costs, brittle routing logic, or disconnected infrastructure pieces.
It grows out of hands-on experience shipping custom AI deployments and building tailored AI solutions where infrastructure decisions, model operations, and cost control could not be left as an afterthought.
The long-term goal of QDivZero is to become a complete AI platform where everything is controlled from the compute layer upward. This private beta is the first step: the foundation on top of which the broader platform will keep expanding.
Private beta access is available here: https://support.valendra.tech/es.
Why QDivZero exists
Most teams do not need another thin wrapper around someone else's API, or a flat-fee endpoint limited to a curated list of models. They need cloud control over where models run, how requests are routed, how risky prompts are stopped, and how retrieval fits into the same stack without paying multiple vendors to move the same data around.
QDivZero was built around a different operating model: capacity pricing instead of token meters, one OpenAI-compatible contract instead of fragmented integrations, and infrastructure-level controls that let teams deploy public or custom Hugging Face models on managed cloud capacity rather than consume a locked menu of hosted APIs. That model comes directly from real delivery work across custom AI rollouts where every deployment had to reconcile performance, governance, and budget in production.
What opens in the private beta
Compute
Compute is the execution layer. It deploys Hugging Face models behind an OpenAI-compatible endpoint, validates runtime requirements, selects capacity across providers, and exposes predictable hourly pricing. Teams get model execution, scheduling, and security controls without turning GPU operations into an internal project.
Smart Balancers
Smart Balancers keep one stable endpoint in front of multiple model groups. They add prompt-based routing, explicit priorities, and fallback behavior so easy prompts can land on cheaper capacity and harder prompts can escalate to stronger paths without changing client integrations.
Flexible Vector Database
Flexible Vector Database turns one retrieval layer into semantic search, image retrieval, and recommendations. Embeddings run in Compute and feed the same catalog layer, so teams do not have to maintain a second vector bill just to make discovery, search, and recommendation surfaces work together.
Firewall
Firewall enforces guardrails before inference starts. Teams attach a firewall slug to OpenAI-compatible requests, combine built-in and custom rules, and choose whether flagged traffic should be blocked immediately or signalled to the model. The goal is simple: stop bad traffic before it reaches paid inference.
Easy Training and Optimization remain in development, but they are not part of the current private beta cohort.
Inference as the connecting layer
Inference is where the platform starts to behave as one system instead of a loose set of features. Compute runs the model, Smart Balancers decide the route, Firewall protects the request before paid execution, and Flexible Vector Database adds retrieval, search, and recommendation context around the same workload.
That complementarity is deliberate. QDivZero is being built so users can start from one inference endpoint and progressively add stronger controls, better routing, richer retrieval, and more capable workflows without changing the core contract each time.
The platform already relies on powerful tools for users, and we will keep extending that layer from the start. The objective is not only to host models, but to give teams a growing operating surface for building, orchestrating, and improving real AI products on top of the same infrastructure base.
What the beta is for
This private beta is designed for AI enthusiasts and product teams that want to validate production cloud architecture, not just run a demo.
- Evaluate capacity pricing against token-metered alternatives.
- Consolidate routing, retrieval, and guardrails into one operating layer.
- Deploy custom or public Hugging Face models behind an OpenAI-compatible API.
- Test semantic search, recommendations, and guarded inference on real workloads.
Built for teams that care about operating leverage
QDivZero is for companies that want fewer invoices, fewer moving pieces, and fewer operational surprises. Instead of stitching together model APIs, vector tooling, safety filters, and routing logic across multiple providers, teams can centralize those decisions in one platform and keep the application contract stable.
That is the real promise of the private beta: not another AI sandbox, but a cleaner production stack.
Request access
Private beta access is being managed directly through Valendra support while we onboard teams in sequence.